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<title>ccv 0.6 open sources near state-of-the-art image classifier under Creative Commons</title>
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<header><h1><a href="/">ccv</a></h1>
<p>A Modern Computer Vision Library</p>
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<section><h1>ccv 0.6 open sources near state-of-the-art image classifier under Creative Commons</h1>
<p>March 27th, 2014</p>
<p>In previous posts, I mentioned that the even numbered release will be bugfixes. However, 0.6 is a bit different.</p>

<p><img src="/photo/2014-03-27-dex.png" alt="now-go-back-and-play-forza" /></p>

<p>For the past one and half year, deep learning, particularly deep convolutional neural network based image classification made waves in the vision community. For a library aiming at providing state-of-the-art implementations, it would be frustrating to not having a competent image classifier implemented after over a year the ground-breaking work published. In the meantime, there are a few open source libraries provided complete (<a href="http://caffe.berkeleyvision.org/">Caffe</a>) / incomplete (<a href="http://cilvr.nyu.edu/doku.php?id=software:overfeat:start">OverFeat</a>, <a href="http://code.google.com/p/cuda-convnet/">cuda-convnet</a>) implementations of the said image classifier. However, all of them are focusing on research related activities (see their licenses: <a href="http://caffe.berkeleyvision.org/getting_pretrained_models.html">1</a>, <a href="https://github.com/sermanet/OverFeat/blob/master/LICENSE">2</a>). Thus, for the past 5 months, I’ve been working on an image classifier in ccv with deep convolutional neural network.</p>

<p>This version’s ccv distributed a image classifier that is trained with ILSVRC 2010 data set of 1000 classes, with top-1 missing rate at 36.83% and top-5 missing rate at 16.25%, thus, close to the state-of-the-art image classifier (Clarifai in ILSVRC 2013 gets top-5 missing rate at 11.19%: <a href="http://www.image-net.org/challenges/LSVRC/2013/results.php">http://www.image-net.org/challenges/LSVRC/2013/results.php</a>, the 16.25% top-5 missing rate is reproduced with ILSVRC 2010 test data set, which is known to be much less challenging). <a href="/doc/doc-convnet">See more about this image classifier</a>.</p>

<p>The license for these data models (the said image classifier, and the pedestrian detectors, the car detector, the face detector) provided in ccv ./samples is changed from <a href="https://raw.github.com/liuliu/ccv/unstable/COPYING">BSD 3-clause license</a> to <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> in hope that this more clarified license will help the adoption of these trained data models.</p>

<p>As always, the new image classifier is available through ccv’s <a href="/doc/doc-http">RESTful interface</a> at http://localhost:3350/convnet/classify. You can also play with ccv at <a href="http://docomputersdream.org/">http://docomputersdream.org/</a></p>

<p>Since this version is an anomaly in terms of release cycle, next two versions of ccv will be devoted to bugfixes and performance improvement. There is also a plan to enter ILSVRC 2014 and publish results on FDDB and LFW for the sake of keeping ccv’s implementation fresh and competitive.</p>

<p>Other changes / bugfixes in ccv 0.6:</p>

<p>1). Moved from hand-written configure script to autoconf (which still provides link / flag information);</p>

<p>2). <a href="http://ci.libccv.org/">http://ci.libccv.org/</a> is online to monitor builds for unstable branch and free of static analyzer reports: <a href="http://ci.libccv.org/analyze/">http://ci.libccv.org/analyze/</a>;</p>

<p>3). Fixed a bug in ./serve/ccv that returned HTTP header claims to be 1.1 but never keeps the connection open;</p>

<p>4). RESTful interface for SWT added Tesseract (OCR) support;</p>

<p>5). Fixed ICF implementation problem with non-standard float point representation;</p>

<p>6). Fixed multi-thread bug with fftw3 usage;</p>

<h3><a href="/">&lsaquo;&nbsp;&nbsp;back&nbsp;</a></h3>
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